Abstract

Feature selection algorithms can reduce the high dimensionality of textual cases and increase case-based task performance. However, conventional algorithms (e.g., information gain) are computationally expensive. We previously showed that, on one dataset, a rough set feature selection algorithm can reduce computational complexity without sacrificing task performance. Here we test the generality of our findings on additional feature selection algorithms, add one data set, and improve our empirical methodology. We observed that features of textual cases vary in their contribution to task performance based on their part-of-speech, and adapted the algorithms to include a part-of-speech bias as background knowledge. Our evaluation shows that injecting this bias significantly increases task performance for rough set algorithms, and that one of these attained significantly higher classification accuracies than information gain. We also confirmed that, under some conditions, randomized training partitions can dramatically reduce training times for rough set algorithms without compromising task performance.